Project Summary/Abstract The long-term objective of this work is the development of a general theory of perceptual categorization and memory that interrelates performance in tasks of classification, identification, and old-new recognition. A major goal is to develop and test formal models that explain the time course of classification and recognition decision making and that explain the development of expertise and skilled performance as a function of extended training. Understanding the nature of perceptual classification and recognition promises to have direct benefits for improving performance in a variety of health-related applications, such as clinical assessment and evaluation techniques and the understanding of cognitive and memory disorders. The specific aims of the present project are to use response-time techniques to contrast the predictions from two main models of perceptual classification: exemplar-based models and rule- based models. According to exemplar models, people represent categories by storing individual examples of the categories in memory and classify objects on the basis of their similarity to these stored examples. According to rule-based models, people engage in a series of logical tests involving the features that compose the objects, and classify the objects on the basis of the outcomes of these tests. In the present research, subjects learn to classify multidimensional stimuli into categories and their response times for making their classification decisions are measured. For the diagnostic category structures that are tested, the rule and exemplar-based models make sharply contrasting predictions of the pattern of response times that should be observed. Among the hypotheses to be investigated is that initial reliance on rule-based strategies will be supplanted by more automatic and efficient forms of exemplar- based retrieval. Similar issues will be investigated in paradigms involving short-term perceptual recognition, in which subjects judge whether presented objects are "old" (previously experienced) or "new." A driving theme of the work is that a unified model based on the storage of individual exemplars may underlie both classification and recognition performance.